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image-analysis — how to use image-analysis how to use image-analysis, image-analysis alternative, image-analysis setup guide, what is image-analysis, image-analysis vs computer vision, image-analysis install for AI agents, visual quality assessment tools, game development with image-analysis, glm-image-gen integration

v1.0.0
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About this Skill

Ideal for Visual Inspection Agents requiring advanced image quality assessment and sprite analysis capabilities. image-analysis is a skill that enables AI agents to assess visual quality of images, including game screenshots and sprites, for quality validation and comparison.

Features

Delegates image analysis tasks to GLM-4.6v subagent
Supports visual quality assessment of game screenshots and sprites
Validates images for blocky backgrounds or transparency issues
Compares assets against Harvest Moon SNES style
Integrates with glm-image-gen for new sprite generation
Enables direct vs delegated analysis via Claude Opus

# Core Topics

OpenSourceSam OpenSourceSam
[0]
[0]
Updated: 3/6/2026

Quality Score

Top 5%
57
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add OpenSourceSam/v2_heras_garden/image-analysis

Agent Capability Analysis

The image-analysis MCP Server by OpenSourceSam is an open-source Categories.community integration for Claude and other AI agents, enabling seamless task automation and capability expansion. Optimized for how to use image-analysis, image-analysis alternative, image-analysis setup guide.

Ideal Agent Persona

Ideal for Visual Inspection Agents requiring advanced image quality assessment and sprite analysis capabilities.

Core Value

Empowers agents to delegate visual quality assessment tasks to GLM-4.6v subagent, utilizing protocols like glm-image-gen for sprite generation and validation, and supporting file formats such as game screenshots and sprites, to ensure high-quality visual assets.

Capabilities Granted for image-analysis MCP Server

Automating visual quality validation for game screenshots
Debugging blocky backgrounds or transparency issues in sprites
Comparing game assets against specific styles, such as Harvest Moon SNES
Validating visual quality before commits
Analyzing newly generated sprites

! Prerequisites & Limits

  • Requires GLM-4.6v subagent integration
  • Specifically designed for game development and testing use cases
  • May require additional configuration for custom image analysis tasks
Project
SKILL.md
6.3 KB
.cursorrules
1.2 KB
package.json
240 B
Ready
UTF-8

# Tags

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SKILL.md
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Image Analysis Skill

Purpose: Delegate image analysis tasks to GLM-4.6v subagent for visual quality assessment of game screenshots and sprites.

When to Use

Invoke this skill when:

  • New sprites are generated (via glm-image-gen)
  • Screenshots captured during playtesting
  • Visual quality validation needed before commits
  • Checking for blocky backgrounds or transparency issues
  • Comparing assets against Harvest Moon SNES style

Direct vs Delegated Analysis

Claude Opus can analyze images directly via the Read tool. Choose the right approach:

ScenarioApproachWhy
1-3 imagesClaude DirectNo overhead, faster
Quick quality checkClaude DirectImmediate feedback
Batch analysis (10+)MiniMax ParallelToken efficiency
Extensive visual QAMiniMax Parallel50x cheaper

Claude Direct Pattern

Read image file → Analyze in-context → Continue workflow

MiniMax/GLM Delegation Pattern (for batches)

Spawn parallel agents → Each analyzes subset → Aggregate results

Default: Use Claude Direct for small tasks, delegate batches to save tokens.

Quick Start

Analyze Screenshot

bash
1mcp__4_5v_mcp__analyze_image \ 2 --imageSource "temp/screenshots/[filename].png" \ 3 --prompt "Analyze this game screenshot for visual quality. Check for: blocky backgrounds, tiling seams, sprite transparency issues, pixel art scaling problems, overall Harvest Moon SNES readability. Provide specific, actionable feedback."

Analyze Sprite Asset

bash
1mcp__4_5v_mcp__analyze_image \ 2 --imageSource "assets/sprites/placeholders/[filename].png" \ 3 --prompt "Analyze this pixel art sprite for Harvest Moon SNES style quality. Check: transparency (clean edges), silhouette (strong/readable), proportions (compact not stretched), color palette (dusk-friendly), pixel detail (minimal noise). Reference: assets/sprites/PLACEHOLDER_ASSET_SPEC.txt"

Analyze NPC Sprite

bash
1mcp__4_5v_mcp__analyze_image \ 2 --imageSource "assets/sprites/placeholders/npc_[name].png" \ 3 --prompt "Analyze this 64x64 NPC sprite. Target proportions: ~48h x 32w centered. Check: strong silhouette, Mediterranean theme alignment, clean transparency, no elongation. Harvest Moon SNES style reference."

Standard Prompts

Full Quality Assessment

Analyze this image for Circe's Garden game quality standards:

Visual Style:
- Harvest Moon SNES readability (clean pixel art, strong silhouettes)
- Stardew Valley influence (but more mythic/moody)
- Mediterranean ancient Greek island theme
- Dusk-friendly palette (muted, not oversaturated)
- 32px tiles, 2x camera zoom

Check For:
1. Background issues: blocky patterns, tiling seams, color banding
2. Transparency: clean edges (no halos), proper alpha channel
3. Pixel art: minimal noise, clear details, proper scaling
4. Proportions: compact bodies, not stretched/elongated
5. Colors: natural earth tones, muted jewel accents
6. Lighting: soft ambient, no harsh specular
7. Silhouette: strong, recognizable at small size

Provide specific feedback: what works, what needs fixing, file references.

Transparency Check (Focused)

Check this sprite for transparency issues:
- Halo effect (light/dark ring around edges)
- Color bleeding (background showing through)
- Harsh edges (no smooth curves on diagonals)
- Partial transparency (should be opaque or transparent, not in-between)

Report specific pixel locations and severity.

Style Comparison

Compare this asset to Harvest Moon SNES style:
- Clean pixel art with minimal noise
- Strong silhouette (recognizable at 32x32)
- Soft, natural color palette
- Proper alpha blending
- No anti-aliasing on edges
- Cozy, approachable aesthetic

Report what matches style and what deviates.

Output Format

Expected response should include:

markdown
1## Analysis Summary 2[One-line assessment: PASS / NEEDS WORK / FAIL] 3 4### Issues Found 5**Critical (P0):** 6- [Specific issue with location] 7 8**Important (P1):** 9- [Issue description] 10 11**Minor (P2):** 12- [Minor detail] 13 14### What Works 15- [Positive aspects to preserve] 16 17### Recommendations 181. [Actionable fix 1] 192. [Actionable fix 2]

Common Issues

IssueDetectionFix
Blocky backgroundVisible pixel grid, tiling patternsRegenerate with better seam handling
Halo effectLight/dark ring around spriteFix alpha channel, clean edges
Color bleedingBackground color in transparent pixelsRe-export with proper transparency
Stretched proportionsElongated body/limbsRegenerate with compact proportions
Oversaturated colorsToo bright/vibrantAdjust palette to dusk-friendly tones
Poor silhouetteNot recognizable at small sizeStrengthen outline, simplify details

File Locations

Input:

  • Screenshots: temp/screenshots/
  • Sprites: assets/sprites/
  • Placeholders: assets/sprites/placeholders/

Reference:

  • Asset specs: assets/sprites/PLACEHOLDER_ASSET_SPEC.txt
  • Style guide: assets/sprites/PLACEHOLDER_README.md

Integration

With glm-image-gen

bash
1# After generating new sprite 2./generate-image.sh '{"subject":"..."}' 1024x1024 standard 3./process-image.sh "$TEMP" "32x32" "assets/sprites/output.png" 4 5# Then analyze quality 6mcp__4_5v_mcp__analyze_image --imageSource "assets/sprites/output.png" --prompt "[standard prompt]"

With HPV (Playtesting)

bash
1# During screenshot review 2ls temp/screenshots/ 3mcp__4_5v_mcp__analyze_image --imageSource "temp/screenshots/world_map.png" --prompt "[screenshot prompt]"

Pre-commit Check

bash
1# Before committing asset changes 2git status 3mcp__4_5v_mcp__analyze_image --imageSource "assets/sprites/new_asset.png" --prompt "[validation prompt]"

Subagent Role

Full subagent configuration: .claude/roles/image-analysis-subagent.md

Model: GLM-4.6v (vision-capable) Tool: mcp__4_5v_mcp__analyze_image Purpose: Specialized visual quality assessment

Success Criteria

Analysis is effective when:

  • Issues match human findings (80%+ accuracy)
  • False positives < 20%
  • Actionable feedback provided
  • Response time < 30 seconds
  • Consistent report format

Related:

  • .claude/skills/glm-image-gen/SKILL.md (sprite generation)
  • assets/sprites/PLACEHOLDER_ASSET_SPEC.txt (asset specifications)
  • .claude/roles/image-analysis-subagent.md (full subagent docs)

[Kimi Code CLI - 2026-01-26]

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